A two-phase method for extracting explanatory arguments from Bayesian networks

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Principles and Practice of Explainable Machine Learning (2020, background): “Relevant works include [80], where the authors propose a way to construct explanatory arguments from Bayesian models, as well as [52], where explanations are produced in order to assess the trustworthiness of a model.”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “Probabilistically Supported Arguments (PSA) [307] is a two-phase method for extracting probabilistically explanatory supported arguments from a bayesian network.”